The invention relates to an electrical grid monitoring, prediction, analysis, and control system and, more particularly, to an improved system and method for grid control using a multi-dimensional multi-layered cellular computational network (CCN) to provide a situational awareness (SA)/situational intelligence (SI) framework. CCNs can model/replicate complex dynamical systems, and thus can be rich in dynamics. The invention uses this SA/SI framework to provide a perception of environmental elements within a volume of time and space, an understanding of their meaning, and an estimation of their current states and prediction in the near future.
Situational awareness is critical and important to control center/room operators for secure, reliable and efficient power system and smart grid operation. Despite the importance of analytical methods, continuous data sense-making is critical for ensuring the stability and security of the power system. SA/SI systems provide an understanding of the spatial and temporal dynamics of an interconnected and geographical distributed power system.
Current grid control predictive systems are slow to react and often fail to take all-important variables into account when modeling specific actions or outcomes. Data is provided from a multitude of sources in real-time or at near-real-time speeds. The problem is not generally the amount of data available to operators. The problem is more often how that data is analyzed and used to inform operators. In other words, more data and/or information does not necessarily matter in critical operations. What is important is that the data is properly analyzed and prioritized to provide useful information, knowledge and understanding of critical issues (circumstances/conditions) to the operator in a respective time and space (network region).
It is also critical that the operator have a full understanding of the electric grid's connectivity with neighboring systems at a given time and space because the grid, at least in the United States, is a highly interconnected system. This means that the dynamics of each part of the grid are spatially and temporally connected/coupled.
Typically, when a disturbance in the grid occurs, the system operator in the control center of an electricity utility is thinking one or more of the following thoughts and looking for immediate answers to inform his/her next action(s).
1. Have I received a new alert?
2. Is any system limit in violation?
3. If so, how bad is the violation?
4. Where is the problem location?
5. What is the cause?
6. Is there any possible immediate corrective or mitigative action?
7. If yes, what is that action?
8. Can or should the action be immediately implemented or can it wait?
9. Has the problem been addressed?
10. Is there any follow up action needed?
Answers to these questions can only be achieved with greater awareness and intelligence, respectively, in control rooms and control centers. Field data must be converted to information and knowledge, then routed to appropriate control centers and operators. Again, the problem is not data acquisition because current supervisory control and data acquisition (SCADA) systems are able to receive data at fast rates (typically 4 to 5 seconds). The problem is big data analytics for smart grid where there is high volume of different types (voltages, active and reactive powers, speed deviations of generators, frequency, etc.) of data streaming at very fast speed (from phasor measurement units (PMUs) at 30 Hz to 120 Hz). The problem is how that big data is processed in real-time, or even faster than real-time, to inform the operator, especially of near-future-state predictions and projections, also known as situational intelligence, to take appropriate secure, reliable and efficient actions. An SA/SI system and method, properly implemented, would have the ability to address these issues. In other words, the actionable information is critical to understand future control states for the system. The invention does this by helping operators to understand these states creating a predictive state model for individual electrical devices and the electrical grid overall.
What is needed is a system that can address these concerns and questions in a holistic manner, keeping in context the fact that local regions of the grid are viewed microscopically, while the grid overall is viewed macroscopically. The system must provide SA/SI for operators in control rooms and control centers so that the operators can make the correct decision under difficult circumstances to maintain a high degree of grid integrity and reliability.
What is needed is a system that can use SA/SI to properly analyze multiple system and control state variables within a volume of time and space to provide an understanding of their meaning and predict their states in the near future where these multiple variables can have different timescales.
What is also needed is a system that can integrate historical and real-time data to implement near-future SA and SI, where intelligence (near-future) is a function of history, current state, and predicted state. Such a system should be able to predict security and stability limits such as, but not limited to, real-time operating conditions, dynamic models, forecast load, forecast generation, and contingency analyses.
What is further needed is a system that provides advanced visualization to integrate all applications as well as topology updates and geographical influences. These visualizations should be in real-time or even “faster than real-time” to provide situational awareness and situational intelligence of power system operations in advance within a virtual power system.